Proteomics and Proteogenomics - Revision history

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2022-01-07T18:54:25Z

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2021-12-06T22:27:25Z

Debra Tabron at 17:37, 3 December 2021

2021-12-03T17:37:03Z

Jhurley: /* Assessing Changes in Microbial Community Composition and Dynamics – Common Applications */

2021-12-01T15:30:35Z

‎Assessing Changes in Microbial Community Composition and Dynamics – Common Applications

Jhurley: /* Assessing Changes in Microbial Community Composition and Dynamics – Common Applications */

2021-12-01T15:28:44Z

‎Assessing Changes in Microbial Community Composition and Dynamics – Common Applications

Jhurley: /* Targeted vs Shotgun Proteomics */

2021-12-01T15:24:31Z

‎Targeted vs Shotgun Proteomics

Jhurley: /* Sample Collection, Preservation, and Shipping */

2021-12-01T15:17:34Z

‎Sample Collection, Preservation, and Shipping

Jhurley: /* Assessing Changes in Microbial Community Composition and Dynamics – Common Applications */

2021-12-01T15:03:22Z

‎Assessing Changes in Microbial Community Composition and Dynamics – Common Applications

Jhurley: /* Assessing Changes in Microbial Community Composition and Dynamics – Common Applications */

2021-12-01T15:02:23Z

‎Assessing Changes in Microbial Community Composition and Dynamics – Common Applications

Jhurley: /* Introduction */

2021-12-01T14:40:09Z

‎Introduction

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 Proteomics is the analysis of proteins present in a sample. Proteogenomics is the combined use of proteomics with genomics and transcriptomics to support protein identifications and analyses. As tools, proteomics and proteogenomics allow researchers and practitioners to understand the functional gene products and relevant microbial metabolisms in a system, which in turn can lead to informed decision-making in remediation situations.
 <div style="float:right;margin:0 0 2em 2em;">__TOC__</div>
 '''Related Article(s):'''

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−	'''Contributor(s):''' Dr. Kate H. Kucharzyk, Dr. Morgan V. Evans, Dr. Robert W. Murdoch, Dr. Fadime ~~Kara~~ Murdoch and Larry Mullins	+	'''Contributor(s):''' [[Dr. Kate Kucharzyk]], [[Dr. Morgan Evans]], [[Dr. Robert Murdoch]], [[Dr. Fadime Murdoch]], and [[Larry Mullins]]

@@ Line 44: / Line 44: @@
 #'''Bioenergy''' – characterization of feedstock conversion into energy e.g., cellulose or lignin degradation to biofuels<ref name="Ndimba2013"> Ndimba, B.K., Ndimba, R.J., Johnson, T.S., Waditee-Sirisattha, R., Baba, M., Sirisattha, S., Shiraiwa, Y., Agrawal, G.K., and Rakwal, R, 2013. Biofuels as a sustainable energy source: an update of the applications of proteomics in bioenergy crops and algae Journal of Proteomics, 20(93), pp. 234-244. [https://doi.org/10.1016/j.jprot.2013.05.041 doi: 10.1016/j.jprot.2013.05.041]</ref>
 #'''Human health''' – characterization of microbial involvement in impact/control of disease versus health in human bodies<ref name="Brooks2015">Brooks, B., Mueller, R.S., Young, J.C., Morowitz, M.J., Hettich, R.L., and Banfield, J.F., 2015. Strain-resolved microbial community proteomics reveals simultaneous aerobic and anaerobic function during gastrointestinal tract colonization of a preterm infant. Frontiers in Microbiology, 1(6) pp. 654. [https://doi.org/10.3389/fmicb.2015.00654 doi: 10.3389/fmicb.2015.00654] [[Media: Brooks2015.pdf | Article pdf]]</ref><ref name="Carr2014">Carr, S.A., Abbatiello, S.E., Ackermann, B.L., Borchers, C., Domon, B., Deutsch, E.W.,, Grant, R.P., Hoofnagle, A.N., Hüttenhain, R., Koomen, J.M., Liebler, D.C., Liu, T., MacLean, B., Mani, D.R., Mansfield, E., Neubert, H., Paulovich, A.G., Reiter, L., Vitek, O., Aebersold, R., Anderson, L., Bethem, R., Blonder, J., Boja, E., Botelho, J., Boyne, M., Bradshaw, R.A., Burlingame, A.L., Chan, D., Keshishian, H., Kuhn, E., Kinsinger, C., Lee, J.S., Lee, S.W., Moritz, R., Oses-Prieto, J., Rifai, N., Ritchie, J., Rodriguez, H., Srinivas, P.R., Townsend, R.R., Van Eyk, J., Whiteley, G., Wiita, A., and Weintraub, S., 2014.  Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Molecular and Cellular Proteomics, 13(3), pp. 907-917. [https://doi.org/10.1074/mcp.M113.036095 doi: 10.1074/mcp.M113.036095] [[Media: Carr2014.pdf | Article pdf]]</ref>
-#'''Bioremediation''' – characterization of degradation of contaminants in sediments, soils, and groundwater by microorganisms<ref name="Bansal2009">Bansal, R., Deobald, L.A., Crawford, R.L., Paszczynski, A.J., 2009. Proteomic detection of proteins involved in perchlorate and chlorate metabolism. Biodegradation, 20(5), pp.603-620. [https://doi.org/10.1007/s10532-009-9248-0 doi: 10.1007/s10532-009-9248-0]</ref><ref name="Fuller2020">Fuller, M. E., van Groos, P. G. K., Jarrett, M., Kucharzyk, K. H., Minard-Smith, A., Heraty, L. J., and Sturchio, N. C., 2020. Application of a multiple lines of evidence approach to document natural attenuation of hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX) in groundwater. Chemosphere. 250, pp. 126210. [https://doi.org/10.1016/j.chemosphere.2020.126210 doi: 10.1016/j.chemosphere.2020.126210]</ref><ref name="Kucharzyk2020">Kucharzyk, K. H., Meisel, J. E., Kara-Murdoch, F., Murdoch, R. W., Higgins, S. A., Vainberg, S., and Löffler, F. E., 2020. Metagenome-guided proteomic quantification of reductive dehalogenases in the Dehalococcoides mccartyi-containing consortium SDC-9. Journal of Proteome Research, 9(4), pp. 1812-1823. [https://doi.org/10.1021/acs.jproteome.0c00072 doi: 10.1021/acs.jproteome.0c00072]</ref><ref name="Kucharzyk2018">Kucharzyk, K.H., Rectanus, H.V., Bartling, C., Chang, P., Rosansky. S., Neil, K., and Chaudhry, T., 2018. Assessment of Post Remediation Performance of a Biobarrier Oxygen Injection System at a Methyl Tert-Butyl Ether (MTBE)-Contaminated Site, Marine Corps Base Camp Pendleton
+#'''Bioremediation''' – characterization of degradation of contaminants in sediments, soils, and groundwater by microorganisms<ref name="Bansal2009">Bansal, R., Deobald, L.A., Crawford, R.L., Paszczynski, A.J., 2009. Proteomic detection of proteins involved in perchlorate and chlorate metabolism. Biodegradation, 20(5), pp.603-620. [https://doi.org/10.1007/s10532-009-9248-0 doi: 10.1007/s10532-009-9248-0]</ref><ref name="Fuller2020">Fuller, M. E., van Groos, P. G. K., Jarrett, M., Kucharzyk, K. H., Minard-Smith, A., Heraty, L. J., and Sturchio, N. C., 2020. Application of a multiple lines of evidence approach to document natural attenuation of hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX) in groundwater. Chemosphere. 250, pp. 126210. [https://doi.org/10.1016/j.chemosphere.2020.126210 doi: 10.1016/j.chemosphere.2020.126210]</ref><ref name="Kucharzyk2020">Kucharzyk, K. H., Meisel, J. E., Kara-Murdoch, F., Murdoch, R. W., Higgins, S. A., Vainberg, S., and Löffler, F. E., 2020. Metagenome-guided proteomic quantification of reductive dehalogenases in the Dehalococcoides mccartyi-containing consortium SDC-9. Journal of Proteome Research, 9(4), pp. 1812-1823. [https://doi.org/10.1021/acs.jproteome.0c00072 doi: 10.1021/acs.jproteome.0c00072]</ref><ref name="Kucharzyk2018">Kucharzyk, K.H., Rectanus, H.V., Bartling, C., Chang, P., Rosansky. S., Neil, K., and Chaudhry, T., 2018. Assessment of Post Remediation Performance of a Biobarrier Oxygen Injection System at a Methyl Tert-Butyl Ether (MTBE)-Contaminated Site, Marine Corps Base Camp Pendleton San Diego, California. Environmental Security Technology Certification Program (ESTCP), Alexandria, VA. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Emerging-Issues/ER-201588 ER-201588]. [[Media: Kucharzyk2018.pdf | Report pdf]]</ref><ref name="Michalsen2020a"> Michalsen, M. M., Kucharzyk, K. H., Bartling, C., Meisel, J. E., Hatzinger, P., Wilson, J., Istok, J., and Loffler, F, 2020. Validation of advanced molecular biological tools to monitor chlorinated solvent bioremediation and estimate cVOC degradation rates. Environmental Security Technology Certification Program, Alexandria, VA. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-201726 ER-201726]. [[Media: Michalsen2020a.pdf | Report pdf]]</ref><ref name="Michalsen2020b">Michalsen, M. M., King, A. S., Istok, J. D., Crocker, F. H., Fuller, M. E., Kucharzyk, K. H., and Gander, M. J., 2020. Spatially distinct redox conditions and degradation rates following field-scale bioaugmentation for RDX-contaminated groundwater remediation. Journal of Hazardous Materials, 387, 121529. [https://doi.org/10.1016/j.jhazmat.2019.121529 doi: 10.1016/j.jhazmat.2019.121529]</ref>
 #'''Carbon cycling''' – characterization of the role of microorganisms in carbon flow in an ecosystem.
 #'''Agricultural metabolism''' – characterization of microbial interactions with plants<ref name="Tan2017">Tan, B.C, Lim, Y.S., and Lau, S.E., 2017. Proteomics in commercial crops: An overview. Journal of Proteomics, 169, pp. 176-188. [https://doi.org/10.1016/j.jprot.2017.05.018 doi:10.1016/j.jprot.2017.05.018]</ref>

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 #'''Bioenergy''' – characterization of feedstock conversion into energy e.g., cellulose or lignin degradation to biofuels<ref name="Ndimba2013"> Ndimba, B.K., Ndimba, R.J., Johnson, T.S., Waditee-Sirisattha, R., Baba, M., Sirisattha, S., Shiraiwa, Y., Agrawal, G.K., and Rakwal, R, 2013. Biofuels as a sustainable energy source: an update of the applications of proteomics in bioenergy crops and algae Journal of Proteomics, 20(93), pp. 234-244. [https://doi.org/10.1016/j.jprot.2013.05.041 doi: 10.1016/j.jprot.2013.05.041]</ref>
 #'''Human health''' – characterization of microbial involvement in impact/control of disease versus health in human bodies<ref name="Brooks2015">Brooks, B., Mueller, R.S., Young, J.C., Morowitz, M.J., Hettich, R.L., and Banfield, J.F., 2015. Strain-resolved microbial community proteomics reveals simultaneous aerobic and anaerobic function during gastrointestinal tract colonization of a preterm infant. Frontiers in Microbiology, 1(6) pp. 654. [https://doi.org/10.3389/fmicb.2015.00654 doi: 10.3389/fmicb.2015.00654] [[Media: Brooks2015.pdf | Article pdf]]</ref><ref name="Carr2014">Carr, S.A., Abbatiello, S.E., Ackermann, B.L., Borchers, C., Domon, B., Deutsch, E.W.,, Grant, R.P., Hoofnagle, A.N., Hüttenhain, R., Koomen, J.M., Liebler, D.C., Liu, T., MacLean, B., Mani, D.R., Mansfield, E., Neubert, H., Paulovich, A.G., Reiter, L., Vitek, O., Aebersold, R., Anderson, L., Bethem, R., Blonder, J., Boja, E., Botelho, J., Boyne, M., Bradshaw, R.A., Burlingame, A.L., Chan, D., Keshishian, H., Kuhn, E., Kinsinger, C., Lee, J.S., Lee, S.W., Moritz, R., Oses-Prieto, J., Rifai, N., Ritchie, J., Rodriguez, H., Srinivas, P.R., Townsend, R.R., Van Eyk, J., Whiteley, G., Wiita, A., and Weintraub, S., 2014.  Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Molecular and Cellular Proteomics, 13(3), pp. 907-917. [https://doi.org/10.1074/mcp.M113.036095 doi: 10.1074/mcp.M113.036095] [[Media: Carr2014.pdf | Article pdf]]</ref>
-#'''Bioremediation''' – characterization of degradation of contaminants in sediments, soils, and groundwater by microorganisms<ref name="Bansal2009">Bansal, R., Deobald, L.A., Crawford, R.L., Paszczynski, A.J., 2009. Proteomic detection of proteins involved in perchlorate and chlorate metabolism. Biodegradation, 20(5), pp.603-620. [https://doi.org/10.1007/s10532-009-9248-0 doi: 10.1007/s10532-009-9248-0]</ref><ref name="Fuller2020">Fuller, M. E., van Groos, P. G. K., Jarrett, M., Kucharzyk, K. H., Minard-Smith, A., Heraty, L. J., and Sturchio, N. C., 2020. Application of a multiple lines of evidence approach to document natural attenuation of hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX) in groundwater. Chemosphere. 250, pp. 126210. [https://doi.org/10.1016/j.chemosphere.2020.126210 doi: 10.1016/j.chemosphere.2020.126210]</ref><ref name="Kucharzyk2020">Kucharzyk, K. H., Meisel, J. E., Kara-Murdoch, F., Murdoch, R. W., Higgins, S. A., Vainberg, S., and Löffler, F. E., 2020. Metagenome-guided proteomic quantification of reductive dehalogenases in the Dehalococcoides mccartyi-containing consortium SDC-9. Journal of Proteome Research, 9(4), pp. 1812-1823. [https://doi.org/10.1021/acs.jproteome.0c00072 doi: 10.1021/acs.jproteome.0c00072]</ref><ref name="Kucharzyk2018">Kucharzyk, K.H., Rectanus, H.V., Bartling, C., Chang, P., Rosansky. S., Neil, K., and Chaudhry, T., 2018. Assessment of post remediation performance of a biobarrier oxygen injection system at a methyl tert-butyl ether (MTBE)-contaminated site, Marine Corps Base Camp Pendleton San Diego, California. Environmental Security Technology Certification Program, Alexandria, VA. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Emerging-Issues/ER-201588 ER-201588]. [[Media: Kucharzyk2018.pdf | Report pdf]]</ref><ref name="Michalsen2020a"> Michalsen, M. M., Kucharzyk, K. H., Bartling, C., Meisel, J. E., Hatzinger, P., Wilson, J., Istok, J., and Loffler, F, 2020. Validation of advanced molecular biological tools to monitor chlorinated solvent bioremediation and estimate cVOC degradation rates. Environmental Security Technology Certification Program, Alexandria, VA. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-201726 ER-201726]. [[Media: Michalsen2020a.pdf | Report pdf]]</ref><ref name="Michalsen2020b">Michalsen, M. M., King, A. S., Istok, J. D., Crocker, F. H., Fuller, M. E., Kucharzyk, K. H., and Gander, M. J., 2020. Spatially distinct redox conditions and degradation rates following field-scale bioaugmentation for RDX-contaminated groundwater remediation. Journal of Hazardous Materials, 387, 121529. [https://doi.org/10.1016/j.jhazmat.2019.121529 doi: 10.1016/j.jhazmat.2019.121529]</ref>
+#'''Bioremediation''' – characterization of degradation of contaminants in sediments, soils, and groundwater by microorganisms<ref name="Bansal2009">Bansal, R., Deobald, L.A., Crawford, R.L., Paszczynski, A.J., 2009. Proteomic detection of proteins involved in perchlorate and chlorate metabolism. Biodegradation, 20(5), pp.603-620. [https://doi.org/10.1007/s10532-009-9248-0 doi: 10.1007/s10532-009-9248-0]</ref><ref name="Fuller2020">Fuller, M. E., van Groos, P. G. K., Jarrett, M., Kucharzyk, K. H., Minard-Smith, A., Heraty, L. J., and Sturchio, N. C., 2020. Application of a multiple lines of evidence approach to document natural attenuation of hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX) in groundwater. Chemosphere. 250, pp. 126210. [https://doi.org/10.1016/j.chemosphere.2020.126210 doi: 10.1016/j.chemosphere.2020.126210]</ref><ref name="Kucharzyk2020">Kucharzyk, K. H., Meisel, J. E., Kara-Murdoch, F., Murdoch, R. W., Higgins, S. A., Vainberg, S., and Löffler, F. E., 2020. Metagenome-guided proteomic quantification of reductive dehalogenases in the Dehalococcoides mccartyi-containing consortium SDC-9. Journal of Proteome Research, 9(4), pp. 1812-1823. [https://doi.org/10.1021/acs.jproteome.0c00072 doi: 10.1021/acs.jproteome.0c00072]</ref><ref name="Kucharzyk2018">Kucharzyk, K.H., Rectanus, H.V., Bartling, C., Chang, P., Rosansky. S., Neil, K., and Chaudhry, T., 2018. Assessment of Post Remediation Performance of a Biobarrier Oxygen Injection System at a Methyl Tert-Butyl Ether (MTBE)-Contaminated Site, Marine Corps Base Camp Pendleton
 #'''Carbon cycling''' – characterization of the role of microorganisms in carbon flow in an ecosystem.
 #'''Agricultural metabolism''' – characterization of microbial interactions with plants<ref name="Tan2017">Tan, B.C, Lim, Y.S., and Lau, S.E., 2017. Proteomics in commercial crops: An overview. Journal of Proteomics, 169, pp. 176-188. [https://doi.org/10.1016/j.jprot.2017.05.018 doi:10.1016/j.jprot.2017.05.018]</ref>

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 #'''Targeted proteomics,''' focused on identifying and absolutely quantifying one protein.
 '''''Shotgun proteomics''''' refers to digestion of the total proteome and subjecting all resulting peptides to separation, mass-spectroscopy, and identification based on a reference protein database (ideally derived from in silico translation of the (meta)genome).
-Notably, shotgun proteomics generates only an approximated ''relative abundance'' for identified proteins. On the other hand, ''targeted proteomics'' allows for ''absolute quantification'' of a single protein within a complex sample, which in turn allows for analysis of any potential correlation to a degradation rate. Prediction of degradation rates based on enzyme concentration is a crucial step towards better understanding of the molecular events underlying metabolic processes. By measuring key biomarkers, proteomic studies present an opportunity to gain profound into ecosystem health, degradation of recalcitrant compounds, and bioremediation. Quantitative proteomics can also guide regulatory agencies to make better site management decisions, thereby minimizing radiation costs and chemically induced adverse effects.
+Notably, shotgun proteomics generates only an approximated ''relative abundance'' for identified proteins. On the other hand, ''targeted proteomics'' allows for ''absolute quantification'' of a single protein within a complex sample, which in turn allows for analysis of any potential correlation to a degradation rate. Prediction of degradation rates based on enzyme concentration is a crucial step towards better understanding of the molecular events underlying metabolic processes. By measuring key biomarkers, proteomic studies present an opportunity to gain profound insight into ecosystem health, degradation of recalcitrant compounds, and bioremediation. Quantitative proteomics can also guide regulatory agencies to make better site management decisions, thereby minimizing radiation costs and chemically induced adverse effects.
 '''''Targeted Proteomics''''' targets peptides of a specific protein in a complex mixture of other peptides and determines their presence (if they are above the detection limit) and quantity in one sample or across multiple samples (Figure 1)<ref name="Zhang2013"/>. This analysis usually utilizes a triple quadrupole mass spectrometer (QqQ-MS), an instrument which has traditionally been used to quantify small molecules. Only recently has it been utilized for peptides. Parallel Reaction Monitoring (PRM) and data independent analysis are alternative options that can be far cheaper than developing a method for a QqQ-MS.
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 PSMs are inherently statistically uncertain. A given shotgun proteome may involve hundreds of thousands of PSMs, which leads to the danger of false discovery. Traditionally, a formal false discovery rate (FDR) is applied; this is an adjustment of the threshold for statistical significance based on the number of tests performed, i.e. the more PSMs, the more stringent the testing must be.  The risk is greatly exacerbated by using reference protein databases that do not reflect the sample. Use of the (meta)genome correlating to the sample under study makes this danger of false identification much less risky when compared to traditional approaches.  It is generally acknowledged that rigid adherence to FDR thresholds is a serious impediment to thorough PSM assignment<ref name="Heyer2017"> Heyer, R., Schallert, K., Zoun, R., Becher, B., Saake, G., and Benndorf, D., 2017. Challenges and perspectives of metaproteomic data analysis. Journal of Biotechnology, 261, pp. 24–36. [https://doi.org/10.1016/j.jbiotec.2017.06.1201 doi: 10.1016/j.jbiotec.2017.06.1201] [[Media: Heyer2017.pdf | Article pdf]]</ref> but is generally advisable when analyzing a proteome without any pre-existing knowledge on sample composition.
 Following PSM assignment, whether by use of a global reference protein database with application of FDR or by comparison to a sample-specific reference (meta)genome, peptides are matched to proteins. This step acts as a second statistical filtering step and can employ several criteria such as whether the peptide identified is unique in the database, how many peptides match a given protein, and what score was assigned to the PSMs by the PSM algorithm. A metaproteome protein identification might, for example, require three or six matching PSMs.
-[[File: ProteomicsFig2.png|thumb|660px|Figure 2. Bottom- up shotgun proteomics]]
+[[File: ProteomicsFig2.png|thumb|660px|Figure 2. Bottom-up shotgun proteomics]]
 Software for making PSMs and protein identifications can be obtained as individual packages, but several convenient open-source packages are available, some of which include several PSM algorithms (SearchGUI) and even pipelines for making further sample comparisons (PatternLab for Proteomics) or functional interpretations (MetaProteomeAnalyzer). Many of these features are also available in commercial software packages, such as Progenesis QI (Waters) and ProteinPilot (SCIEX).  Global bottom-up shotgun proteomics data analysis remains an actively evolving discipline.
 ==Selecting Sample Locations==
 Below are a few guidelines for selecting sampling locations to aid in drawing conclusions from metaproteomics data.

← Older revision	Revision as of 18:54, 7 January 2022
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 *'''Plume:''' These samples are collected from distinct zones within the source area or contaminant plume to reflect variations in contaminant concentrations, geochemical conditions, and other site-specific criteria.
 ==Sample Collection, Preservation, and Shipping==
-Sampling procedures for proteomics analyses are straightforward and readily integrated into existing monitoring programs. Almost any type of sample matrix (soil, sediment, groundwater), filters (on-site filtration) can be analyzed. All samples should be shipped to the laboratory on ice or dry ice (-20 °C) using an overnight carrier to minimize the potential for changes in the microbial community between collection and analysis and keep integrity of proteins.
+Sampling procedures for proteomics analyses are straightforward and readily integrated into existing monitoring programs. Almost any type of sample matrix (soil, sediment, groundwater) or filters (on-site filtration) can be analyzed. All samples should be shipped to the laboratory on ice or dry ice (-20 °C) using an overnight carrier to minimize the potential for changes in the microbial community between collection and analysis and keep integrity of proteins.
 Groundwater samples (typically 1 L) can be shipped directly to the laboratory or filtered in the field. For on-site filtration, groundwater is pumped through a Sterivex<sup>&reg;</sup> or Bio-Flo<sup>&reg;</sup> filter using standard low flow sampling techniques. The groundwater may then be discarded appropriately. As with other sample types, filters should be shipped on ice (4 °C) using an overnight carrier.
 ==References==
 <references />

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 Environmental metaproteomics is used in applied research areas such as:
 #'''Bioenergy''' – characterization of feedstock conversion into energy e.g., cellulose or lignin degradation to biofuels<ref name="Ndimba2013"> Ndimba, B.K., Ndimba, R.J., Johnson, T.S., Waditee-Sirisattha, R., Baba, M., Sirisattha, S., Shiraiwa, Y., Agrawal, G.K., and Rakwal, R, 2013. Biofuels as a sustainable energy source: an update of the applications of proteomics in bioenergy crops and algae Journal of Proteomics, 20(93), pp. 234-244. [https://doi.org/10.1016/j.jprot.2013.05.041 doi: 10.1016/j.jprot.2013.05.041]</ref>
-#'''Human health''' – characterization of microbial involvement in impact/control of disease vs health in human bodies<ref name="Brooks2015">Brooks, B., Mueller, R.S., Young, J.C., Morowitz, M.J., Hettich, R.L., and Banfield, J.F., 2015. Strain-resolved microbial community proteomics reveals simultaneous aerobic and anaerobic function during gastrointestinal tract colonization of a preterm infant. Frontiers in Microbiology, 1(6) pp. 654. [https://doi.org/10.3389/fmicb.2015.00654 doi: 10.3389/fmicb.2015.00654] [[Media: Brooks2015.pdf | Article pdf]]</ref><ref name="Carr2014">Carr, S.A., Abbatiello, S.E., Ackermann, B.L., Borchers, C., Domon, B., Deutsch, E.W.,, Grant, R.P., Hoofnagle, A.N., Hüttenhain, R., Koomen, J.M., Liebler, D.C., Liu, T., MacLean, B., Mani, D.R., Mansfield, E., Neubert, H., Paulovich, A.G., Reiter, L., Vitek, O., Aebersold, R., Anderson, L., Bethem, R., Blonder, J., Boja, E., Botelho, J., Boyne, M., Bradshaw, R.A., Burlingame, A.L., Chan, D., Keshishian, H., Kuhn, E., Kinsinger, C., Lee, J.S., Lee, S.W., Moritz, R., Oses-Prieto, J., Rifai, N., Ritchie, J., Rodriguez, H., Srinivas, P.R., Townsend, R.R., Van Eyk, J., Whiteley, G., Wiita, A., and Weintraub, S., 2014.  Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Molecular and Cellular Proteomics, 13(3), pp. 907-917. [https://doi.org/10.1074/mcp.M113.036095 doi: 10.1074/mcp.M113.036095] [[Media: Carr2014.pdf | Article pdf]]</ref>
+#'''Human health''' – characterization of microbial involvement in impact/control of disease versus health in human bodies<ref name="Brooks2015">Brooks, B., Mueller, R.S., Young, J.C., Morowitz, M.J., Hettich, R.L., and Banfield, J.F., 2015. Strain-resolved microbial community proteomics reveals simultaneous aerobic and anaerobic function during gastrointestinal tract colonization of a preterm infant. Frontiers in Microbiology, 1(6) pp. 654. [https://doi.org/10.3389/fmicb.2015.00654 doi: 10.3389/fmicb.2015.00654] [[Media: Brooks2015.pdf | Article pdf]]</ref><ref name="Carr2014">Carr, S.A., Abbatiello, S.E., Ackermann, B.L., Borchers, C., Domon, B., Deutsch, E.W.,, Grant, R.P., Hoofnagle, A.N., Hüttenhain, R., Koomen, J.M., Liebler, D.C., Liu, T., MacLean, B., Mani, D.R., Mansfield, E., Neubert, H., Paulovich, A.G., Reiter, L., Vitek, O., Aebersold, R., Anderson, L., Bethem, R., Blonder, J., Boja, E., Botelho, J., Boyne, M., Bradshaw, R.A., Burlingame, A.L., Chan, D., Keshishian, H., Kuhn, E., Kinsinger, C., Lee, J.S., Lee, S.W., Moritz, R., Oses-Prieto, J., Rifai, N., Ritchie, J., Rodriguez, H., Srinivas, P.R., Townsend, R.R., Van Eyk, J., Whiteley, G., Wiita, A., and Weintraub, S., 2014.  Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Molecular and Cellular Proteomics, 13(3), pp. 907-917. [https://doi.org/10.1074/mcp.M113.036095 doi: 10.1074/mcp.M113.036095] [[Media: Carr2014.pdf | Article pdf]]</ref>
 #'''Bioremediation''' – characterization of degradation of contaminants in sediments, soils, and groundwater by microorganisms<ref name="Bansal2009">Bansal, R., Deobald, L.A., Crawford, R.L., Paszczynski, A.J., 2009. Proteomic detection of proteins involved in perchlorate and chlorate metabolism. Biodegradation, 20(5), pp.603-620. [https://doi.org/10.1007/s10532-009-9248-0 doi: 10.1007/s10532-009-9248-0]</ref><ref name="Fuller2020">Fuller, M. E., van Groos, P. G. K., Jarrett, M., Kucharzyk, K. H., Minard-Smith, A., Heraty, L. J., and Sturchio, N. C., 2020. Application of a multiple lines of evidence approach to document natural attenuation of hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX) in groundwater. Chemosphere. 250, pp. 126210. [https://doi.org/10.1016/j.chemosphere.2020.126210 doi: 10.1016/j.chemosphere.2020.126210]</ref><ref name="Kucharzyk2020">Kucharzyk, K. H., Meisel, J. E., Kara-Murdoch, F., Murdoch, R. W., Higgins, S. A., Vainberg, S., and Löffler, F. E., 2020. Metagenome-guided proteomic quantification of reductive dehalogenases in the Dehalococcoides mccartyi-containing consortium SDC-9. Journal of Proteome Research, 9(4), pp. 1812-1823. [https://doi.org/10.1021/acs.jproteome.0c00072 doi: 10.1021/acs.jproteome.0c00072]</ref><ref name="Kucharzyk2018">Kucharzyk, K.H., Rectanus, H.V., Bartling, C., Chang, P., Rosansky. S., Neil, K., and Chaudhry, T., 2018. Assessment of post remediation performance of a biobarrier oxygen injection system at a methyl tert-butyl ether (MTBE)-contaminated site, Marine Corps Base Camp Pendleton San Diego, California. Environmental Security Technology Certification Program, Alexandria, VA. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Emerging-Issues/ER-201588 ER-201588]. [[Media: Kucharzyk2018.pdf | Report pdf]]</ref><ref name="Michalsen2020a"> Michalsen, M. M., Kucharzyk, K. H., Bartling, C., Meisel, J. E., Hatzinger, P., Wilson, J., Istok, J., and Loffler, F, 2020. Validation of advanced molecular biological tools to monitor chlorinated solvent bioremediation and estimate cVOC degradation rates. Environmental Security Technology Certification Program, Alexandria, VA. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-201726 ER-201726]. [[Media: Michalsen2020a.pdf | Report pdf]]</ref><ref name="Michalsen2020b">Michalsen, M. M., King, A. S., Istok, J. D., Crocker, F. H., Fuller, M. E., Kucharzyk, K. H., and Gander, M. J., 2020. Spatially distinct redox conditions and degradation rates following field-scale bioaugmentation for RDX-contaminated groundwater remediation. Journal of Hazardous Materials, 387, 121529. [https://doi.org/10.1016/j.jhazmat.2019.121529 doi: 10.1016/j.jhazmat.2019.121529]</ref>
 #'''Carbon cycling''' – characterization of a role of microorganisms in carbon flow in an ecosystem.

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 ==Introduction==
-'''Proteomics''' is the comprehensive analysis of the proteins produced by single organisms or by microbial community (e.g., “meta”-proteomics). In this regard, proteomics represents the identification of functional gene products, providing information and insight into structural proteins and the molecular machinery produced and utilized by organisms to sustain the metabolic processes. While proteomics data can be analyzed in a de novo manner by comparing to global protein sequence databases, from a systems biology perspective, the ideal starting point for all considerations and the key enabling information is the (meta)genome of the sample under study.
+'''Proteomics''' is the comprehensive analysis of the proteins produced by single organisms or by a microbial community (e.g., “meta”-proteomics). In this regard, proteomics represents the identification of functional gene products, providing information and insight into structural proteins and the molecular machinery produced and utilized by organisms to sustain the metabolic processes. While proteomics data can be analyzed in a ''de novo'' manner by comparing to global protein sequence databases, from a systems biology perspective, the ideal starting point for all considerations and the key enabling information is the (meta)genome of the sample under study.
 '''Proteogenomics''' combines proteomics with (meta)genomics and/or transcriptomics to better analyze and identify proteins<ref name="Helbling2012">Helbling, D.E., Ackermann, M., Fenner, K., Kohler, H.P., and Johnson, D.R., 2012.  The activity level of a microbial community function can be predicted from its metatranscriptome. The ISME Journal, 6(4), pp. 902-904. [https://doi.org/10.1038/ismej.2011.158 doi: 10.1038/ismej.2011.158] [[Media: Helbling2012.pdf | Article pdf]]</ref>. For environmental studies, proteomics can be applied to soil, groundwater, sediment, or other environmental samples. Proteomics allows for functional characterization of a sample, enabling investigators to infer what relevant metabolical pathways may be active in a system (e.g., hydrocarbon degradation, reductive dechlorination). The large-scale characterization of any given proteome is accomplished by comparing measured peptide spectra with predicted protein or peptide data derived from (meta)genomic information. Thus, it is vital to have complete (meta)genome sequence information for the system being studied <ref name="Ansong2008">Ansong C., Purvine, S.O., Adkins, J.N., Lipton, M.S., and Smith, R.D., 2008.  Proteogenomics: needs and roles to be filled by proteomics in genome annotation. Briefings in Functional Genomics, 7(1), pp. 50-62. [https://doi.org/10.1093/bfgp/eln010 doi: 10.1093/bfgp/eln010] [[Media: Ansong2008.pdf | Article pdf]]</ref>.This approach coined the term proteogenomics which describes the strong linkage between genomics and proteomics. As implied, the quality of the proteomic measurements is inextricably linked to the quality of the genomic or metagenomic sequence data. Proteogenomics is an inherently more uncertain technique when compared to nucleic acid sequencing or qPCR technologies, yet it provides unparalleled global insights into biological structure and function.
-DNA-based methods such as shotgun metagenomics and 16S rRNA amplicon sequencing provide important information and guidance for potential function of microbial communities. Like shotgun proteomics, both methods also provide <u>relative abundances of many features</u>. On the other hand, quantitative real time PCR (qPCR) provides <u>absolute quantities of few features</u> with greater sensitivity (at least 1 order of magnitude) than metagenomics approaches<ref name="Clark2018">Clark K., Taggart, D.M., Baldwin, B.R., Ritalahti, K.M., Murdoch, R.W., Hatt, J.K., and Löffler, F.E., 2018.  Normalized Quantitative PCR Measurements as Predictors for Ethene Formation at Sites Impacted with Chlorinated Ethenes. Environmental Science & Technology, 52(22), pp. 13410-13420. [https://doi.org/10.1021/acs.est.8b04373 doi: 10.1021/acs.est.8b04373]</ref>.As with all nucleic-acid-based (eg. DNA or RNA) methods, qPCR only informs potential activity, not actual activity. By detecting gene expression rather than simply genes, RNA-based methods such as metatranscriptomics provide insight into which genes are active<ref name="Czaplicki2016">Czaplicki, L.M. and Gunsch, C.K., 2016.  Reflection on Molecular Approaches Influencing State-of-the-Art Bioremediation Design: Culturing to Microbial Community Fingerprinting to Omics. Journal of Environmental Engineering, 142(10), pp. 1-13. [https://doi.org/10.1061/(ASCE)EE.1943-7870.0001141 doi: 10.1061/(ASCE)EE.1943-7870.0001141]</ref>, but at the cost of additional challenges posed by increased difficulty with RNA isolation and instability. Proteomics provides the actual catalytic activity by detection and quantification of proteins of interest.
+DNA-based methods such as shotgun metagenomics and 16S rRNA amplicon sequencing provide important information and guidance for potential function of microbial communities. Like shotgun proteomics, both methods also provide <u>relative abundances of many features</u>. On the other hand, quantitative real time PCR (qPCR) provides <u>absolute quantities of few features</u> with greater sensitivity (at least 1 order of magnitude) than metagenomics approaches<ref name="Clark2018">Clark K., Taggart, D.M., Baldwin, B.R., Ritalahti, K.M., Murdoch, R.W., Hatt, J.K., and Löffler, F.E., 2018.  Normalized Quantitative PCR Measurements as Predictors for Ethene Formation at Sites Impacted with Chlorinated Ethenes. Environmental Science & Technology, 52(22), pp. 13410-13420. [https://doi.org/10.1021/acs.est.8b04373 doi: 10.1021/acs.est.8b04373]</ref>. As with all nucleic-acid-based (eg. DNA or RNA) methods, qPCR only informs potential activity, not actual activity. By detecting gene expression rather than simply genes, RNA-based methods such as metatranscriptomics provide insight into which genes are active<ref name="Czaplicki2016">Czaplicki, L.M. and Gunsch, C.K., 2016. Reflection on Molecular Approaches Influencing State-of-the-Art Bioremediation Design: Culturing to Microbial Community Fingerprinting to Omics. Journal of Environmental Engineering, 142(10), pp. 1-13. [https://doi.org/10.1061/(ASCE)EE.1943-7870.0001141 doi: 10.1061/(ASCE)EE.1943-7870.0001141]</ref>, but at the cost of additional challenges posed by increased difficulty with RNA isolation and instability. Proteomics provides the actual catalytic activity by detection and quantification of proteins of interest.
 ==Advantages==